Author Identifier (ORCID)
Lai Chang Zhang: https://orcid.org/0000-0003-0661-2051
Abstract
In this study, a Ti1.5Nb1Ta0.5Zr1Mo0.5 (TNTZM) high-entropy alloy was fabricated using laser powder bed fusion (LPBF). By integrating 63 sets of parameter trials with machine learning (ML) models, an optimised process window was identified, achieving a density of up to 99.9%. The combination of relatively high laser power and low scanning speed resulted in the formation of a stable cellular structure. Subsequent heat treatments at 700, 850, and 1000°C showed that while small-angle misorientations developed at cell-wall interfaces and medium-entropy (Ti–Zr–Mo) second-phase particles precipitated preferentially in the cell walls, the overall cellular architecture remained intact. Mechanical testing showed that these heat-treated samples exhibited yield strengths over 150 MPa higher than the as-built samples, while still retaining nearly 50% ductility under short-term heat treatment. In particular, small-angle grain boundaries and nanoscale second-phase particles together reinforce the cell walls and promote intracellular dislocation accumulation, thereby improving the overall mechanical properties of the alloy. These results demonstrate that combining ML-guided process design with targeted heat treatment is an effective method for additive manufacturing of refractory HEAs with high density and mechanical properties.
Document Type
Journal Article
Date of Publication
1-1-2025
Volume
20
Issue
1
Publisher
Taylor & Francis
School
Centre for Advanced Materials and Manufacturing / School of Engineering
RAS ID
83444
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Comments
Jiang, D., Luo, M., Liu, C., Zhang, Y., Zhang, L., Wang, K., Wang, W., Xie, L., Wang, L., Lu, W., & Zhang, D. (2025). 3D Printing parameter optimisation combined with heat treatment for achieving high density and enhanced performance in refractory high-entropy alloys. Virtual and Physical Prototyping, 20(1). https://doi.org/10.1080/17452759.2025.2524524